A recent article in the Personal Finance section of the Wall Street Journal had a prescription for anxious investors that Andy has been talking about for more than a year: consider asset allocation funds. Our Global Macro separate account has been very popular, partly because it allows investors to get into the market in a way that can be conservative when needed, but one that doesn’t lock investors into a product that can only be conservative.

The stock market’s powerful rally over the past year has gone a long way toward reducing the losses that many mutual-fund investors suffered in late 2007 and 2008.

But the rebound—with the Standard & Poor’s 500-stock index up 74% from its March 9, 2009, low—has done nothing for one group of investors: those who bailed out of stocks and have remained on the sidelines. Some of these investors have poured large sums into bond funds, even though those holdings may take a beating whenever interest rates rise from today’s unusually low levels, possibly later this year. Some forecasters, meanwhile, believe that stocks may finish 2010 up as much as 10%.

So, for investors who want to step back into stocks but are still anxious, here’s a modest suggestion: You don’t have to take your stock exposure straight up. You can dilute it by buying an allocation fund that spreads its assets across many market sectors, from stocks and bonds to money-market instruments and convertible securities.

While the WSJ article is a good general introduction to the idea, I think there are a few caveats that should be mentioned.

There’s still a big difference between a strategic asset allocation fund and a tactical asset allocation fund.

Many [asset allocation funds] keep their exposures within set ranges, while others may vary their mix widely.

Your fund selection will probably depend a lot on the individual client. A strategic asset allocation fund will more often have a tight range or even a fixed or target allocation for stocks or bonds. This can often target the volatility successfully–but can hurt returns if the asset classes themselves are out of favor. Tactical funds will more often have broader ranges or be unconstrained in terms of allocations. This additional flexibility can lead to higher returns, but it could be accompanied by higher volatility.

One thing the article does not mention at all, unfortunately, is that you also have a choice between a purely domestic asset allocation fund or a global asset allocation fund. A typical domestic asset allocation fund will provide anxious investors with a way to ease into the market, but will ignore many of the opportunities in international markets or in alternative assets like real estate, currencies, and commodities. With a variety of possible scenarios for the domestic economy, it might make sense to cast your net a little wider. Still, the article’s main point is valid: an asset allocation fund, especially a global asset allocation fund, is often a good way to deal with a client’s Market Anxiety Disorder and get them back into the game.

—-this article originally appeared 4/7/2010. Investors still don’t like this rally, even though we are a long way down the road from 2010! An asset allocation fund might still be a possible solution.

Rather, it is a post on the inherently unstable nature of correlations between securities and between asset classes. This is important because the success of many of the approaches to portfolio management make the erroneous assumption that correlations are fairly stable over time. I was reminded just how false this belief is while reading The Leuthold Group‘s April Green Book in which they highlighted the rolling 10-year correlations in monthly percentage changes between the S&P 500 and the 10-year bond yield. Does this look stable to you? Chart is shown by permission from The Leuthold Group.

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If you are trying to use this data, would you conclude that higher bond yields are good for the stock market or bad? The answer is that the correlations are all over the map. In 2006, William J. Coaker II published The Volatility of Correlations in the FPA Journal. That paper details the changes in correlations between 15 different asset classes and the S&P 500 over a 34-year time horizon. To give you a flavor for his conclusions, he pointed out that Real Estate’s rolling 5-year correlations to the S&P 500 ranged from 0.17 to 0.75, and for Natural Resources the range was -0.34 to 0.49. History is conclusive – correlations are unstable.

This becomes a big problem for strategic asset allocation models that use historical data to calculate an average correlation between securities or asset classes over time. Those models use that stationary correlation as one of the key inputs into determining how the model should currently be allocated. That may well be of no help to you over the next five to ten years. Unstable correlations are also a major problem for “financial engineers” who use their impressive physics and computer programming abilities to identify historical relationships between securities. They may find patterns in the historical data that lead them to seek to exploit those same patterns in the future (i.e. LTCM in the 1990′s.) The problem is that the future is under no obligation to behave like the past.

Many of the quants are smart enough to recognize that unstable correlations are a major problem. The solution, which I have heard from several well-known quants, is to constantly be willing to reexamine your assumptions and to change the model on an ongoing basis. That logic may sound intelligent, but the reality is that many, if not most, of these quants will end up chasing their tail. Ultimately, they end up in the forecasting game. These quants are rightly worried about when their current model is going to blow up.

Relative strength relies on a different premise. The only historical pattern that must hold true for relative strength to be effective in the future is for long-term trends to exist. That is it. Real estate (insert any other asset class) and commodities (insert any other asset class) can be positively or negatively correlated in the future and relative strength models can do just fine either way. Relative strength models make zero assumptions about what the future should look like. Again, the only assumption that we make is that there will be longer-term trends in the future to capitalize on. Relative strength keeps the portfolio fresh with those securities that have been strong relative performers. It makes no assumptions about the length of time that a given security will remain in the portfolio. Sure, there will be choppy periods here and there where relative strength models do poorly, but there is no need (and it is counterproductive) to constantly tweak the model.

Ultimately, the difference between an adaptive relative strength model and most quant models is as different as a mule is from a horse. Both have four legs, but they are very different animals. One has a high probability of being an excellent performer in the future, while the other’s performance is a big unknown.

—-this article originally appeared 4/16/2010. It’s important to understand the difference between a model that relies on historical correlations and a model that just adapts to current trends.

Mebane Faber recently released a nice white paper, Relative Strength Strategies for Investing, in which he tested relative strength models consisting of US equity sectors from 1926-2009. He also tested relative strength models consisting of global assets like foreign stocks, domestic stocks, bonds, real estate, and commodities from 1973-2009. The relative strength measures that he used for the studies are publicly-known methods based on trailing returns. Some noteworthy conclusions from the paper:

Relative strength models outperformed buy-and-hold in roughly 70% of all years

Approximately 300-600 basis points of outperformance per year was achieved

His relative strength models outperformed in each of the 8 decades studied

I always enjoy reading white papers on relative strength. It is important to mention that the methods of calculating relative strength that were used in Faber’s white paper are publicly-known and have been pointed to for decades by various academics and practitioners. Yet, they continue to work! Those that argue that relative strength strategies will eventually become so popular that they will cease to work have some explaining to do.

—-this article originally appeared 4/20/2010. Of course, the white paper is no longer new at this point, but it is a reminder of the durability of relative strength as a return factor. Every investing method goes through periods of favor and disfavor. Investors are, unfortunately, likely to abandon even profitable methods at the worst possible time. This paper is a good reminder that return factors are durable, but patience may be required to harvest those returns. Most often, the investor that sticks to it will be rewarded.

We’ve all seen numerous studies that purport to show how passive investing is the way to go because you don’t want to be out of the market for the 10 best days. No one ever mentions thatthe “best days” most often occur during the declines!

It turns out that the majority of the best days and the worst days occur near one another, during the declines. Why? Because the market is more volatile during declines. It is true that the market goes down 2-3x as fast as it goes up. (World Beta has a nice post on this topic of volatility clustering, which is where this handy-dandy table comes from.)

You can see how volatility increases and the number of days with daily moves greater than 2.5% really spikes when the market is in a downward trend. It would seem to be a very straightforward proposition to improve your returns simply by avoiding the market when it is in a downtrend.

However, not every strategy can be improved by going to cash. Think about the math: if your investing methodology makes enough extra money on the good days to offset the bad days, or if it can make money during a significant number of the declines, you might be better off just gritting your teeth during the declines and banking the higher returns. Although the table above suggests it should help, a simple strategy of exiting the market (i.e., going to cash) when it is below its 200-day moving average may not always live up to its theoretical billing.

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Consider the graphs above. (The first graph uses linear scaling; the second uses logarithmic scaling for the exact same data.) This test uses Ken French’s database to get a long time horizon and shows the returns of two portfolios constructed with market cap above the NYSE median and in the top 1/3 for relative strength. In other words, the two portfolios are composed of mid- and large-cap stocks with good relative strength. The only difference between the two portfolios is that one (red line) goes to cash when it is below its 200-day moving average. One portfolio (blue line) stays fully invested. The fully invested portfolio turns $100 into $49,577, while the cash-raising portfolio yields only $26,550.

If you would rather forego the extra money in return for less volatility, go right ahead and make that choice. But first stack up 93 boxes of Diamond matches so that you can burn 23,027 $1 bills, one at a time, to represent the difference–and then make your decision.

The drawdowns are less with the 200-day moving average, but it’s not like they are tame–equities will be an inherently volatile asset class as long as human emotions are involved. There are still a couple of drawdowns that are greater than 20%. If an investor is willing to sit through that, they might as well go for the gusto.

As surprising as it may seem, the annualized return over a long period of time is significantly higher if you just stay in the market and bite the bullet during train wrecks–and even two severe bear markets in the last decade have not allowed the 200-day moving average timer to catch up.

At the bottom of every bear market, of course, it certainly feels like it would have been a good idea (in hindsight) to have used the 200-day moving average to get out. In the long run, though, going to cash with a high-performing, high relative strength strategy might be counterproductive. When we looked at 10-year rolling returns, the fully invested high relative strength model has maintained an edge in returns for the last 30 years running.

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Surprising, isn’t it? Counterintuitive results like this are one of the reasons that we find testing so critical. It’s easy to fall in line with the accepted wisdom, but when it is actually put to the test, the accepted wisdom is often wrong. (We often find that even when shown the test data, many people refuse, on principle, to believe it! It is not in their worldview to accept that one of their cherished beliefs could be false.) Every managed portfolio in our Systematic RS lineup has been subjected to heavy testing, both for returns and–and more importantly–for robustness. We have a high degree of confidence that these portfolios will do well in the long run.

—-this article originally appeared 3/5/2010. We find that many investors continue to refuse, on principle, to believe the data! If you have a robust investment method, the idea that you can improve your returns by getting out of the market during downturns appears to be false. (Although it could certainly look true for small specific samples. And, to be clear, 100% invested in a volatile strategy is not the appropriate allocation for most investors.) Volatility can generally be reduced somewhat, but returns suffer. One of our most controversial posts ever—but the data is tough to dispute.

In more recent data, the effect can be seen in this comparison of an S&P 500 ETF and an ETN that switches between the S&P 500 and Treasury bills based on a 200-day moving average system. The volatility has been muted a little bit, but so have the returns.

CXO Advisory has a very interesting blog piece on this topic. They review an academic paper that looks at the way conventional sector rotation is done. Typically, various industry sectors are categorized as early cycle, late cycle, etc. and then you are supposed to own those sectors at that point in the business cycle. Any number of money management firms (not including us) hang their hat on this type of cycle analysis.

In order to determine the potential of traditional sector rotation, the study assumes that you get to have perfect foresight into the business cycle and then you rotate your holdings with the conventional wisdom of when various industries perform best. A couple of disturbing things crop up, given that this is the best you could possibly do with this system.

1) You can squeak by with about 2.3% annual outperformance if you had a crystal ball. If you are even a month or two early or late on the cycle turns, your performance is statistically indistinguishable from zero.

2) 28 of the 48 industries studied (58.3%) underperformed during the times when they were supposed to perform well. There’s obviously enough noise in the system that a sector that is supposed to be strong or weak during a particular part of the cycle often isn’t.

CXO notes, somewhat ironically:

Note that NBER can take as long as two years after a turning point to designate its date and that one business cycle can be very different from another.

In other words, it’s clear that traditional business cycle analysis is not going to help you. You won’t be able to forecast the cycle turning points accurately and the cycles differ so much that industry performance is not consistent.

Sector rotation using relative strength is a big contrast to this. Relative strength makes no a priori assumptions about which industries are going to be strong or weak at various points in the business cycle. A systematic strategy just buys the strong sectors and avoids the weak ones. Lots of studies show that significant outperformance can be earned using relative strength (momentum) with absolutely no insight into the business cycle at all, including some studies done by CXO Advisory. Tactical asset allocation is finally coming into its own and various ways of implementing are available. Business cycle forecasting does not appear to be a feasible way to do it, but relative strength certainly is!

—-this article originally appeared 3/30/2010. Although the link to CXO Advisory is no longer live, you can get the gist of things from the article. Things don’t always perform in the expected fashion, and paying attention to relative strength can be some protection from the problem. Instead of making assumptions about strong or weak performance, relative strength just adapts.

The first question: How many stock market timers, of the several hundred monitored by the Hulbert Financial Digest, called the bottom of the bear market a year ago?

And a follow-up: Of those that did, how many also called the top of the bull market in March 2000 — or, for that matter, the major market turning points in October 2002 and October 2007?

If you are relying on some type of market timing to get you out of the way of bear markets and to get you into bull markets, this is exactly what you want to know. Although there are pundits who claim to have called the bottom to the day, Mr. Hulbert allowed a far more generous window for labeling a market timing call as correct.

… my analysis actually relied on a far more relaxed definition: Instead of moving 100% from cash to stocks in the case of a bottom, or 100% the other way in the case of a top, I allowed exposure changes of just ten percentage points to qualify.

Furthermore, rather than requiring the change in exposure to occur on the exact day of the market’s top or bottom, I looked at a month-long trading window that began before the market’s juncture and extending a couple of weeks thereafter.

That’s a pretty liberal definition: the market timer gets a four-week window and only has to change allocations by 10% to be considered to have “called” the turn. And here’s the bottom line:

Even with these relaxed criteria, however, none of the market timers that the Hulbert Financial Digest has tracked over the last decade were able to call the market tops and bottoms since March 2000.

Yep, zero. [The bold and underline is from me.] It’s not that advisors aren’t trying; it’s just that no one can do it successfully, even with a one-month window and a very modest change in allocations. Obviously, there is lots of hindsight bias going on where advisors claim to have detected market turning points, but when Mr. Hulbert goes back to look at the actual newsletters, not one got it right! You can safely assume anyone who claims to be able to time the market is lying. At the very least, the burden on proof is on them.

We don’t bother trying to figure out what the market will do going forward. We simply follow trends as they present themselves. We use relative strength in a systematic way to identify the trends we want to follow: the strongest ones. We stay with the trend as long as it continues, whether that is for a short time or an extended period. When a trend weakens, as evidenced by its relative strength ranking, we knock that asset out of the portfolio and replace it with a stronger asset. The two white papers we have produced (Relative Strength and Asset Class Rotation and Bringing Real World Testing to Relative Strength) show quite clearly that it is possible to have very favorable investment results over time without any recourse to market timing at all. Discipline and patience are needed, of course, but you don’t have to have a crystal ball.

—-this article originally appeared 3/17/2010. It is especially apropos now that many market pundits are busy predicting a top. It’s certainly possible they are right—but probably equally likely is the proposition that they are just guessing. Over the long run there is weak evidence that market timing is effective.

This is the title of a nice article by Brett Arends at Marketwatch. He points out that a lot of our assumptions, especially regarding risk, are open to question.

Risk is an interesting topic for a lot of reasons, but principally (I think) because people seem to be obsessed with safety. People gravitate like crazy to anything they perceive to be “safe.” (Arnold Kling has an interesting meditation on safe assets here.)

Risk, though, is like matter–it can neither be created nor destroyed. It just exists. When you buy a safe investment, like a U.S. Treasury bill, you are not eliminating your risk; you are just switching out of the risk of losing your money into the risk of losing purchasing power. The risk hasn’t gone away; you have just substituted one risk for another. Good investing is just making sure you’re getting a reasonable return for the risk you are taking.

In general, investors–and people generally–are way too risk averse. They often get snookered in deals that are supposed to be “low risk” mainly because their risk aversion leads them to lunge at anything pretending to be safe. Psychologists, however, have documented that individuals make more errors from being too conservative than too aggressive. Investors tend to make that same mistake. For example, nothing is more revered than a steady-Eddie mutual fund. Investors scour magazines and databases to find a fund that (paradoxically) is safe and has a big return. (News flash: if such a fund existed, you wouldn’t have to look very hard.)

No one goes looking for high-volatility funds on purpose. Yet, according to an article, Risk Rewards: Roller-Coaster Funds Are Worth the Ride at TheStreet.com:

Funds that post big returns in good years but also lose scads of money in down years still tend to do better over time than funds that post slow, steady returns without ever losing much.

The tendency for volatile investments to best those with steadier returns is even more pronounced over time. When we compared volatile funds with less volatile funds over a decade, those that tended to see big performance swings emerged the clear winners. They made roughly twice as much money over a decade.

That’s a game changer. Now, clearly, risk aversion at the cost of long-term returns may be appropriate for some investors. But if blind risk aversion is killing your long-term returns, you might want to re-think. After all, eating Alpo is not very pleasant and Maalox is pretty cheap. Maybe instead of worrying exclusively about volatility, we should give some consideration to returns as well.

—-this article originally appeared 3/3/2010. A more recent take on this theme are the papers of C. Thomas Howard. He points out that volatility is a short-term factors, while compounded returns are a long-term issue. By focusing exclusively on volatility, we can often damage long term results. He re-defines risk as underperformance, not volatility. However one chooses to conceptualize it, blind risk aversion can be dangerous.

Americans, as well as citizens of many other advanced nations, now spend about twice as many years in retirement as they did a generation or two ago. Aggressive saving and adherence to a well-thought-out investment plan are more important today than they have ever been. It is a big mistake for today’s 65-year olds to no longer consider themselves to be “long-term investors.”

—-this article originally appeared 3/1/2010. As you can see from the graphic, the average US 66-year old retiree spends another 15-20 years in retirement. That’s long enough that investment performance is going to be important.

We’ve written about the uselessness of forecasting in the past and even cited James Montier’s wonderful piece, The Seven Sins of Fund Management. This citation comes from Mebane Faber’s World Beta blog. Montier writes:

The two most common biases are over-optimism and overconfidence. Overconfidence refers to a situation whereby people are surprised more often than they expect to be. Effectively people are generally much too sure about their ability to predict. This tendency is particularly pronounced amongst experts. That is to say, experts are more overconfident than lay people. This is consistent with the illusion of knowledge driving overconfidence.

Dunning and colleagues have documented that the worst performers are generally the most overconfident. They argue that such individuals suffer a double curse of being unskilled and unaware of it. Dunning et al argue that the skills needed to produce correct responses are virtually identical to those needed to self-evaluate the potential accuracy of responses. Hence the problem.

This is irony in action. Knowledge drives overconfidence, so people who actually know something about a topic are more prone to think they can forecast, and they probably even sound more believable. And finally, the worst performers are the most overconfident!

This may be one of the few instances in which ignorance is bliss. If you have the Zen “beginner’s mind” and don’t make any assumptions about what might happen, you’re going to be better off than if you are knowledgeable and try to guess.

Systematic trend-following eliminates the need to forecast (although apparently not the desire, since we have clients constantly asking us what we think is going to happen). We use relative strength to drive our trend-following; it is able to pick out the strongest trends, and those are the trends we are interested in following. We stay with an asset as long as it remains strong. When it weakens, we kick it out of the portfolio and replace it with something stronger. This kind of casting-out method allows the portfolio to adapt to the market environment, as it is constantly refreshed with new, strong assets.

Despite having a logical and simple method that performs well over time and eliminates the need to forecast, soothsayers will probably always be with us—but your best bet is to ignore them.

—-this article originally appeared 3/2/2013. Of course the lesson is timeless.

The Leuthold Group’s Doug Ramsey on the psychology that drives bull markets:

Cashing in on bull markets is not a matter of waiting for everything to line up, anyway. There must be a set of intellectually appealing bear arguments keeping some players on the sidelines…it is these same players who will eventually drive prices even higher when “new” and intellectually appealing bull arguments belatedly appear on the scene. I have found that some of the best bull market action occurs when the “bull/bear” arguments superficially appear to be in relative balance, confounding many market players. When the balance tips too heavily to one side or the other, the odds are that most of the related market move is already in the books.

—-this article originally appeared 3/3/2010. Thinking about this paradox is one of the things that led us to start our own sentiment survey focusing on client investment behavior. Even now, many years into the bull market, clients are still behaving fairly cautiously, indicating they do not yet fully believe the bull argument.

I cringe every time I read an article by a value investor that says something like, “You should buy stocks that are on sale, just like you buy [pick your consumer item] on sale.” In the financial markets that can be dangerous.

In a great essay titled, I Want to Buy Losers, Clay Allen of Market Dynamics discusses the problems with this analogy. [You've got to read the whole essay to really appreciate it.]

Many investors buy stocks the way many consumers buy paper towels or any other staple. They are attracted to a sale and loss leaders are a proven method for a retailer to increase the traffic in their store. The value of the item is well known and a sale price gets the attention of potential buyers.

But stocks are not like paper towels. Paper towels can be used to satisfy a need and this is what gives the item its value to the consumer. What gives a stock its value? A stock cannot be used to satisfy a need or accomplish a task. The value of a stock is derived from the financial performance of the company, either actual or expected. The fact that the stock is down in price is usually a sure sign that the financial performance of the company is declining.

…if the value of the stock was constant, then buying bargain stocks would be the correct way to invest in stocks. But stock values are constantly changing as business conditions change for the company and the expectations of investors change.

All in all, it seems to me that relative strength often more closely reflects what the expectations of investors are–and the expectations are what counts. Let’s face it: strong stocks are usually strong because business conditions or fundamentals are good, and weak stocks are usually weak for a reason.

—-this article was originally published 3/26/2010. In the intervening years, my friend Clay Allen has passed away. His wisdom, however, is still with us. His point that a stock is not a paper towel is absolutely correct. The only purpose of an equity investment is to make money.

Investing, at its core, is a simple process. You need to determine if the train is going north or south, or just sitting on a track siding doing nothing. Once you’ve found a train going north, you need only to hop aboard. If the train starts to go south, you need to jump off.

The concept is simple, but sometimes investors make the execution more complicated. For us, relative strength and trend following provide the tools and methodology to find the northbound trains. The same tools and methodology can be used to tell you when the switch engine has come along and started to move the train south.

The problems happen when investors deviate from the simple goal-directed hobo mentality and get too clever for their own good. Can you imagine how irrational some investor behavior must look to a hobo? Here are the top six dysfunctional hobo sayings:

1. I wanted to go north, so I hopped on an out-of-favor southbound train, hoping it would go north eventually. (value hobo)

2. I got on a northbound train, but it only went north a few miles. A switch engine came along and started to take my boxcar south. How embarrassing! This train owes me. I’m not getting off. (ego-attached hobo)

3. There are so many trains going north. I want to hop on one eventually, but I’m afraid it will go south right after I get on it. (failure to launch hobo)

5. I want to go north, but my train pulled on to a siding and stopped. Maybe I’ll just sit here and see what happens. (buy-and-hold hobo)

6. There are so many trains going north without me. Eventually they will all have to go south, and then I’ll have my revenge! (bitter hobo with economics background)

If you want to go north, get on a northbound train. KISS really applies here. On our good days, we all know this, but it’s so easy to forget.

—-this article originally appeared 5/26/2010. Investing need not be complicated. Relative strength investing, in fact, is pretty simple. However, simple is not the same thing as easy! There is a real skill to the disciplined execution of this strategy—or any other strategy.

The table below shows total returns for some of the asset classes they examined.

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What are the commonalities of the best performing assets? 1) Lots of them are highly volatile like emerging markets equities and debt, 2) lots of them are international and thus were a play on the weaker dollar, 3) lots of them were alternative assets like commodities, TIPs, and REITs.

In other words, they were all asset classes that would tend to be marginalized in a traditional strategic asset allocation, where the typical pie would primarily consist of domestic stocks and bonds, with only small allocations to very volatile, international, or alternative assets.

In an interesting way, I think this makes a nice case for tactical asset allocation. While it is true that most investors–just from a risk and volatility perspective–would be unwilling to have a large allocation to emerging markets for an entire decade, they might find that periodic significant exposure to emerging markets during strong trends would be quite acceptable. And even assets near the bottom of the return table like U.S. Treasury bills would have been very welcome in a portfolio during parts of 2008, for example. You can cover the waterfront and just own an equal-weighted piece of everything, but I don’t know if that is the most effective way to do things.

What’s really needed is a systematic method for determining which asset classes to own, and when. Our Systematic Relative Strength process does this pretty effectively, even for asset classes that might be difficult or impossible to grade from a valuation perspective. (How do you determine whether the Euro is cheaper than energy stocks, or whether emerging market debt is cheaper than silver or agricultural commodities?) Once a systematic process is in place, the investor can be slightly more comfortable with perhaps a higher exposure to high volatility or alternative assets, knowing that in a tactical approach the exposures would be adjusted if trends change.

—-this article originally appeared 2/17/2010. There is no telling what the weak or strong assets will be for the coming decade, but I think global tactical asset allocation still represents a reasonable way to deal with that uncertainty.

Rob Arnott is a thought leader in tactical asset allocation, currently well-known for his RAFI Fundamental Indexes. In his recent piece, Lessons from the Naughties, he discusses how investors will need to find return going forward.

The key to better returns will be to respond tactically to the shifting spectrum of opportunity, especially expanding and contracting one’s overall risk budget.

It’s a different way to view tactical asset allocation–looking at it from a risk budget point of view. The general concept is to own risk assets in good markets and safe assets in bad markets.

It turns out that systematic application of relative strength accomplishes this very well. The good folks at Arrow Funds recently asked us to take a look at how the beta in a tactically managed portfolio changed over time. When we examined that issue, it showed that as markets became risky, relative strength reduced the beta of the portfolio by moving toward low volatility (strong) assets. When markets were strong, allocating with relative strength pushed up the beta in the portfolio, thus taking good advantage of the market strength.

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Using relative strength to do tactical asset allocation, the investor was not only able to earn an acceptable rate of return over time, but was able to have some risk mitigation going on the side. That’s a pretty tasty combination in today’s markets.

—-this article originally appeared on 2/26/2010. Amid all of the publicity given recently to risk parity, Arnott’s approach, which is to vary the risk budget over time depending on the opportunities available, has been largely ignored. I think this is unfortunate. His approach, although perhaps not easy, has merit. Tactical asset allocation driven by relative strength is one way to do that.

Barry Ritholtz has posted a 5 minute clip of some of Ben Bernanke’s public comments between 2005-2007 on the housing market and the broader economy. The point of me posting this is not to say that Bernanke is a complete moron because I have little doubt that he is one of the brightest financial minds in the country. However, talk about being dead wrong! If you relied on these opinions in order to make investment decisions, you likely got torched. If you can’t rely on expert opinion when making investment decisions, then what options do you have?

This highlights the value of trend-following systems. Trend following requires zero reliance on expert opinion; it simply allows the investor to adapt to whatever trends the market offers, whether or not experts expected things to play out in a given way. With trend following, you’ll have plenty of losing trades, but you’ll also avoid sitting in losing trades for long periods of time. Furthermore, systematic trend-following has an excellent track record (see here and here.) Trend following allows you to cut your losses short and to hold on to your winners. Frequently, the strongest trends end up being very different from what even the brightest experts predicted.

—-this article originally appeared 2/11/2010. Well, heck, if you can’t trust Ben Bernanke, who can you trust? The answer should be obvious: follow the price trend and forget about the random guessing of experts.

Relative strength calculations rely on a single input: price. We like price because it is a known quantity, not an assumption. In this deconstruction of the Price-to-Earnings Growth (PEG) ratio, the author, Tom Brakke, discusses all of the uncertainties when calculating even a simple ratio like PEG. And amidst all of the uncertainties he mentions is this:

In looking at that calculation, only one of the three variables has any precision: We can observe the market price (P) at virtually any time and be assured that we have an accurate number. The E is a different matter entirely. Which earnings? Forward, trailing, smoothed, operating, adjusted, owner? Why? How deep into accounting and the theory of finance do you want to go?

For most investors, not very far. We like our heuristics clean and easy, not hairy. So, in combining the first two variables we get the P/E ratio, the “multiple” upon which most valuation work rests, despite the questionable assumptions that may be baked in at any time. The addition of the third element, growth (G), gives us not the epiphany we seek, but even more confusion.

The emphasis is mine. This isn’t a knock on fundamental analysis. It can be valuable, but there is an inherent squishiness to it. The only precision is found in price. And price is dynamic: it adapts in real time as expectations of the asset change. (Fundamental data is often available only on a quarterly schedule.) As a result, systematic models built using relative strength adapt quite nicely as conditions change.

—-this article originally appeared 2/10/2010. We still like using prices as an input, especially now that there are so many cross-currents. Every pundit has a different take on what will happen down the road, but prices in a free market will eventually sort it all out.

It’s well known in behavioral finance that investors experience a loss 2-3x more intensely than a gain of the same magnitude. This loss aversion leads investors to avoid even rational bets, according to a Reuters story on a recent study by a Cal Tech scientist.

Laboratory and field evidence suggests that people often avoid risks with losses even when they might earn a substantially larger gain, a behavioral preference termed ‘loss aversion’,” they wrote.

For instance, people will avoid gambles in which they are equally likely to either lose $10 or win $15, even though the expected value of the gamble is positive ($2.50).

The study indicates that people show fear at even the prospect of a loss. Markets are designed to generate fear, not to mention all of the bearish commentators on CNBC. Fear leads to poor decisions, like selling near the bottom of a correction. Unless you are planning to electrically lesion your amygdala, the fear is going to be there–so what’s the best way to deal with it?

The course we have chosen is to make our investment models systematic. That means the decisions are rules-based, not subject to whatever fear the portfolio managers may be experiencing at any given time. Once in a blue moon, excessive caution pays off, but studies suggest that more errors are made being excessively cautious than overly aggressive. A rules-based method treats risk in a even-handed, mathematical way. In other words, take risks that historically are likely to pay off, and keep taking them regardless of your emotional state. Given enough time, the math is likely to swing things in your favor.

—-this article originally appeared 2/10/2010. In the two years since this was written, investors have continued to pay a high price for their fear as the market has continued to advance. There are always scary things around the corner, but a rules-based process can often help you navigate through them. Investors seem to have a hard time learning that scary things don’t necessarily cause markets to perform poorly. In fact, the opposite is often true.

According to a fascinating study discussed in Time Magazine based on 27 million hands of Texas Hold’em, it turns out that the more hands poker players win, the more money they lose! What’s going on here?

I suspect it has to do with investor preferences–gamblers often think the same way. Most people like to have a high percentage of winning trades; they are less happy with a lower percentage of winning trades, even if the occasional winner is a big one. In other words, investors will often prefer a system with 65% winning trades over a system with 45% winning trades, even if the latter method results in much greater overall profits.

People overweigh their frequent small gains vis-à-vis occasional large losses,” Siler says.

In fact, you are generally best off if you cut your losses and let your winners run. This is the way that systematic trend following tends to work. Often this results in a few large trades (the 20% in the 80/20 rule) making up a large part of your profits. Poker players and amateur investors obviously tend to work the other way, preferring lots of small profits–which all tend to be wiped away by a few large losses. Taking lots of small profits is the psychological path of least resistance, but the easy way is the wrong way in this case.

—-this article was originally published 2/10/2010. Investors still have irrational preferences about making money. They usually want profits—but apparently only if they are in a certain distribution! Real life doesn’t work that way. Making money is a fairly messy process. Only a few names turn out to be big winners, so you’ve got to give them a chance to run.

This behavior is not unknown in the stock market, where cognitive biases run unbridled down Wall Street. Ten years ago, everyone was in love with General Electic. It, too, was high-priced and tasted great. Ten years later, GE is considered cheap swill that leaves a bitter taste in the mouths of investors.

The moral of the story is that you can’t fall in love with your stocks or your wine. You have to like it on its own merits. In the case of our Systematic RS accounts, we like a stock only as long as it has high relative strength. When it becomes weaker and drops in its ranking–indicating that other, stronger stocks are available–we sell it and move on to a better class of grape. (We’ve been known to break a bottle here and there, but the idea is to adapt as tastes change.) In this way, we strive to keep our wine cellar stocked with the best vintages all the time.

—-this article originally appeared 2/19/2012. Cognitive biases are still running wild on Wall Street.

Critics of the Efficient Markets Hypothesis continue to get more press. Newsweek’s Barrett Sheridan recently wrote an article that discusses the Efficient Markets Hypothesis (EMH) versus the adaptive-markets hypothesis (AMH). He mentions one of the key flaws in EMH: that market participants are rational.

He goes on to focus on MIT professor Andrew Lo and his AMH work. Lo does not share the EMH tenet that the financial markets consist of cool, calm, and rational investors. He suggests that investors will behave differently depending on their psychology at any given moment. (Some of the old brokers I knew called it the fear-greed pendulum.) It follows that any investment rule based on a fixed measurement of value for the market such as yield, P/E ratio, etc. will work only sporadically over time if the AMH is valid. Nothing is set in stone because investors continually change and adapt to the market ecosystem.

Our Systematic RS portfolios use relative measurements. We believe in an adaptive approach to investing that recognizes that since markets are controlled by real people, they act like real people.

—-this article originally appeared 1/5/2010. Every advisor knows that the risk tolerance of clients changes over the course of a market cycle. I still can’t figure out why anyone thinks that the Efficient Markets Hypothesis ever made sense.

Ken Haman, a managing director at the Advisor Institute at AllianceBernstein responds to the following question posed by an advisor and published in Investment News:

Q: I’ve had some frustrating conversations with clients recently—trying to get them back in the market. Very few are taking my advice, even though they seem to know that staying on the sidelines is a mistake. What’s going on, and how can I get them “unstuck”?

A: Problems like this have to do with how people make decisions. Behavioral finance uses the term “inappropriate extrapolation”–and insights about it can help you understand your clients and respond to them more effectively.

To make any decision, human beings create a mental picture of the future. That’s what “expectations” are–the ability to take information from the past and present, and project it into the future. Unlike most animals, human beings can project far into the future; as a result, we are able to “plan ahead.” Unfortunately, we usually don’t create these future images terribly well. Instead of making a thoughtful assessment of what’s likely to happen in the future, we typically picture the future as just a continuation of the recent past.

Essentially, you want to learn how to install a positive picture of the future that the client feels is likely to happen in reality. Start by explaining the mechanisms of the market and illustrating visually how those mechanisms work. Many investors have only the vaguest understanding of the cause-effect dynamics in the markets. Instead of making thoughtful, well-informed decisions, they react to their perception of patterns and trends. Market “mechanisms” are those cause-effect relationships that equip financial professionals to invest rationally instead of speculating randomly.

By looking at how market mechanisms operated in both the recent and more distant past, you teach your clients how to think more strategically about the markets. This allows them to build a more vivid mental picture of market behaviors in the future. Make sure you explain market mechanisms visually as well as verbally: use charts and graphs that show market behaviors over time. Whenever possible, connect your investment recommendations to a clear explanation of the mechanism that is involved.

Second, provide an adequate level of detail about the mechanisms you explain. There’s a commonly held myth that clients aren’t interested in hearing about the markets. So, many financial advisors gloss over important information and rush to their proposal without creating a case the client understands. But clients are interested in understanding the mechanisms that drive their investment results–as long as your explanation is clearly illustrated and easy to understand.

Finally, you have to deliver your message with personal conviction–that you fully believe the future will look the way you anticipate. Your clients need to borrow your conviction and clarity about the future. That’s how they’ll build their sense of confidence in the decisions you’re asking them to make. Take a stand on what you believe about the future, and add the courage of your own convictions to the clarity of your explanation.

There is also an alternative approach of just being frank with the client and telling them that you don’t know exactly what the future holds, nor does anyone else. However, you adhere to a systematic relative strength process that gives you great flexibility to allocate to a wide range of asset classes depending on how the future unfolds. At times, the approach can be allocated very conservatively and at times it can be allocated quite aggressively. My experience has been that clients appreciate the honesty and are willing to embrace a trend-following approach that deals very effectively with not being able to see into the future.

—-this article originally appeared 1/12/2010. More than two years later, many clients are still on the sidelines. Many of them definitely do engage in inappropriate extrapolation! An advisor’s first duty is to be honest, but you’ve got to do it in a way that is motivating and not paralyzing.

The annals of investor behavior make for some pretty scary reading. Yet this story from the Wall Street Journal may take the cake. It is an article about the top-performing mutual fund of the decade and it shows with remarkable clarity how badly investors butcher their long-term returns. The article hits the premise right up front:

Meet the decade’s best-performing U.S. diversified stock mutual fund: Ken Heebner’s $3.7 billion CGM Focus Fund, which rose more than 18% annually and outpaced its closest rival by more than three percentage points.

Too bad investors weren’t around to enjoy much of those gains. The typical CGM Focus shareholder lost 11% annually in the 10 years ending Nov. 30, according to investment research firm Morningstar Inc.

It’s hard to know whether to laugh or cry. In a brutal decade, Mr. Heebner did a remarkable job, gaining 18% per year for his investors. The only investment acumen required to reap this 18% return was leaving the fund alone. Yet in the single best stock fund of the decade investors managed to misbehave and actually lose substantial amounts of money—11% annually.

I beg to differ. It’s really hard to use well?? What does that even mean? If it is, it’s only in the sense that a pet rock is really hard to care for.

Investor note: actively managed or adaptive products need to be left alone! The whole idea of an active or adaptive product is that the manager will handle things for you, instead of you having to do it yourself.

Unfortunately, there is an implicitbelief among investors—and their advisors—that they can do a better job than the professionals running the funds, but every single study shows that belief to be false. There is not one study of which I am aware that shows retail investors (or retail investors assisted by advisors) outperforming professional investors. So where does that widespread belief come from?

From the biggest bogeyman in behavioral finance: overconfidence. Confidence is a wonderful trait in human beings. It gets us to attempt new things and to grow. From an evolutionary point of view, it is probably quite adaptive. In the financial arena, it’s a killer. Like high blood pressure, it’s a silent killer too, because no one ever believes they are overconfident.

At a Harvard conference on behavioral finance, I heard Nobel Prize winner Daniel Kahneman talk about the best way to combat overconfidence. He suggested intentionally taking what he called an “outside view.” Instead of placing yourself—with all of your incredible and unique talents and abilities—in the midst of the situation, he proposed using an outside individual, like your neighbor, for instance. Instead of asking, “What are the odds that I can quit my day job and open a top-performing hedge fund or play in the NBA?” ask instead, “What are the odds that my neighbor (the plumber, or the realtor, or the unemployed MBA) can quit his day job and open a top performing hedge fund or play in the NBA?” When you put things in an outside context like that, they always seem a lot less likely according to Kahneman. We all think of ourselves as special; in reality, we’re pretty much like everyone else.

Why, then, are investors so quick to bail out on everyone else? Overconfidence again. Our generally mistaken belief that we are special makes everyone else not quite as special as us. Overconfidence and belief in our own specialness makes us frame things completely differently: when we have a bad quarter, it was probably bad luck on a couple of stock picks; if Bill Miller (to choose a recent example) has a bad quarter, it’s probably because he’s lost his marbles and his investment process is irretriveably broken. We’d better bail out, fast. (A lot of people came to that conclusion over the past couple of years. In 2009, Legg Mason Value Trust was +40.6%, more than 14% ahead of its category peers.)

Think about an adaptive Dorsey, Wright Research model like DALI. As conditions change, it attempts to adapt by changing its holdings. Does it make sense to jump in and out of DALI depending on what happened last quarter or last year? Of course not. You either buy into the tactical approach or you don’t. Once you decide to buy into—presumably because you agree with the general premise—a managed mutual fund, a managed account, or an active index, for goodness sakes, leave it alone.

In financial markets, overconfidence is the enemy of patience. Overconfidence is expensive; patience with managed products can be quite rewarding. In the example of the CGM Focus Fund, Mr. Heebner grew $10,000 into $61,444 over the course of the last ten years. Investors in the fund, compounding at -11% annually, turned $10,000 into $3,118. The difference of $58,326 is the dollar value of patience in black and white.

—-this article originally appeared 1/6/2010. Unfortunately, human nature has not changed in the last two years! Investors still damage their returns with their impatience. Try not to be one of them!

The Journal of Indexes has the entire current issue devoted to articles on this topic, along with the best magazine cover ever. (Since it is, after all, the Journal of Indexes, you can probably guess how they came out on the active versus passive debate!)

One article by Craig Israelson, a finance professor at Brigham Young University, stood out. He discussed what he called “actively passive” portfolios, where a number of passive indexes are managed in an active way. (Both of the mutual funds that we sub-advise and our Global Macro separate account are essentially done this way, as we are using ETFs as the investment vehicles.) With a mix of seven asset classes, he looks at a variety of scenarios for being actively passive: perfectly good timing, perfectly poor timing, average timing, random timing, momentum, mean reversion, buying laggards, and annual rebalancing with various portfolio blends. I’ve clipped one of the tables from the paper below so that you can see the various outcomes:

Click to enlarge

Although there is only a slight mention of it in the article, the momentum portfolio (you would know it as relative strength) swamps everything but perfect market timing, with a terminal value more than 3X the next best strategy. Obviously, when it is well-executed, a relative strength strategy can add a lot of return. (The rebalancing also seemed to help a little bit over time and reduced the volatility.)

Maybe for Joe Retail Investor, who can’t control his emotions and/or his impulsive trading, asset allocation and rebalancing is the way to go, but if you have any kind of reasonable systematic process and you are after returns, the data show pretty clearly that relative strength should be the preferred strategy.

Man versus machine, art versus science, intuition versus logic—all of these are ways of expressing what we often think of as contradictory approaches to problem solving. Should we be guided more by data and precedent, or is it more important to allow for the human element? Is it critical to be able to step aside and say, with the benefit of our judgment, “maybe this time really is different?”

A huge body of research has clarified much about how intuition works, and how it doesn’t. Here’s some of what we’ve learned:

It takes a long time to build good intuition. Chess players, for example, need 10 years of dedicated study and competition to assemble a sufficient mental repertoire of board patterns.

Intuition only works well in specific environments, ones that provide a person with good cues and rapid feedback . Cues are accurate indications about what’s going to happen next. They exist in poker and firefighting, but not in, say, stock markets. Despite what chartists think, it’s impossible to build good intuition about future market moves because no publicly available information provides good cues about later stock movements. [Needless to say, I don't agree with his assessment of stock charts!] Feedback from the environment is information about what worked and what didn’t. It exists in neonatal ICUs because babies stay there for a while. It’s hard, though, to build medical intuition about conditions that change after the patient has left the care environment, since there’s no feedback loop.

We apply intuition inconsistently. Even experts are inconsistent. One study determined what criteria clinical psychologists used to diagnose their patients, and then created simple models based on these criteria. Then, the researchers presented the doctors with new patients to diagnose and also diagnosed those new patients with their models. The models did a better job diagnosing the new cases than did the humans whose knowledge was used to build them. The best explanation for this is that people applied what they knew inconsistently — their intuition varied. Models, though, don’t have intuition.

We can’t know or tell where our ideas come from. There’s no way for even an experienced person to know if a spontaneous idea is the result of legitimate expert intuition or of a pernicious bias. In other words, we have lousy intuition about our intuition.

It’s easy to make bad judgments quickly. We have many biases that lead us astray when making assessments. Here’s just one example. If I ask a group of people “Is the average price of German cars more or less than $100,000?” and then ask them to estimate the average price of German cars, they’ll “anchor” around BMWs and other high-end makes when estimating. If I ask a parallel group the same two questions but say “more or less than $30,000″ instead, they’ll anchor around VWs and give a much lower estimate. How much lower? About $35,000 on average, or half the difference in the two anchor prices. How information is presented affects what we think.

We’ve written before about how long it takes to become world-class. Most studies show that it takes about ten years to become an expert if you apply yourself diligently. Obviously, the “intuition” of an expert is much better than the intuition of a neophyte. If you think about that for a minute, it’s pretty clear that intuition is really just judgment in disguise. The expert is better than the novice simply because they have a bigger knowledge base and more experience.

Really, the art versus science debate is over and the machines have won it going away. Nowhere is this more apparent than in chess. Chess is an incredibly complex mental activity. Humans study with top trainers for a decade to achieve excellence. There is no question that training and practice can cause a player to improve hugely, but it is still no contest. As processing power and programming experience has become more widespread, a $50 CD-ROM off-the-shelf piece of software can defeat the best players in the world in a match without much problem. Most of the world’s top grandmasters now use chess software to train with and to check their ideas. (In fact, so do average players since the software is so cheap and ubiquitous.)

How did we get to this state of affairs? Well, the software now incorporates the experience and judgment of many top players. Their combined knowledge is much more than any one person can absorb in a lifetime. In addition, the processing speed of a standard desktop computer is now so fast that no human can keep it with it. It doesn’t get tired, upset, nervous, or bored. Basically, you have the best of both worlds—lifetimes of human talent and experience applied with relentless discipline.

A 2000 paper on clinical versus mechanical prediction by Grove, Zald, Lebow, Snitz, & Nelson had the following abstract:

>The process of making judgments and decisions requires a method for combining data. To compare the accuracy of clinical and mechanical (formal, statistical) data-combination techniques, we performed a meta-analysis on studies of human health and behavior. On average, mechanical-prediction techniques were about 10% more accurate than clinical predictions. Depending on the specific analysis, mechanical prediction substantially outperformed clinical prediction in 33%–47% of studies examined. Although clinical predictions were often as accurate as mechanical predictions, in only a few studies (6%–16%) were they substantially more accurate. Superiority for mechanical-prediction techniques was consistent, regardless of the judgment task, type of judges, judges’ amounts of experience, or the types of data being combined. Clinical predictions performed relatively less well when predictors included clinical interview data. These data indicate that mechanical predictions of human behaviors are equal or superior to clinical prediction methods for a wide range of circumstances.

That’s a 33-47% win rate for the scientists and a 6-16% win rate for the artists, and that was ten years ago. That’s not really very surprising. Science is what has allowed us to develop large-scale agriculture, industrialize, and build a modern society. Science and technology are not without their problems, but if the artists have stayed in charge we might still be living in caves, although no doubt we would have some pretty awesome cave paintings.

This is the thought process behind our Systematic Relative Strength accounts. We were able to codify our own best judgment, include lifetimes of other experience from investors we interviewed or relative strength studies that we examined, and have it all run in a disciplined fashion. We chose relative strength because it was the best-performing factor and also because, since it is relative, it is adaptive. There is always cooperation between man and machine in our process, but moving more toward data-driven decisions is indeed the future of decision making.

—-this article originally appeared 1/15/2010. Our thought process hasn’t changed—we still believe that a systematic, adaptive investment process is the way to go.

If you have money left over after paying your bills, you fall into the category of “investor.” You could invest your surplus money in having a good time in Vegas, a mattress, a bank savings account, or any manner of financial instruments. Some investments have a financial return; others only a psychic return if you are lucky.

Most people invest for a simple reason: to provide income when they are no longer able to work. Some people might actually want to retire, so they invest to provide income for the time after they voluntarily choose to stop working. To get from “investor” status to actual retirement status, a few difficult things have to happen correctly.

1. You actually need to save money. And you have to save a lot. In today’s America, this means becoming a cultural outlaw and foregoing some current consumption. Welcome to the radical underground.

2. You need to save the money in assets that produce income or capital gains. (Income-producing assets are nice, but capital gains can be spent just as effectively.) These assets are often volatile, leveraged like real estate, or intangible like stocks and bonds. Scary stuff, in other words. Investing your surplus funds in Budweiser, while it may confer certain social benefits, will not provide a retirement income.

3. You need to manage not to muck up your returns. The DALBAR numbers don’t lie. To earn decent real returns, you need to select quality money managers and/or funds and then leave them to do their work.

4. You need to be able to do realistic math. For example, most people think their home is a great investment—but they never subtract from the returns all of the property taxes and maintenance that are required, or remove the effects of leverage. Every study that does shows that homes are not a good financial investment. In addition, in order to make a projection of how much money you will require to retire, you need to be able to make a reasonable estimate of your real net-net-net returns (after inflation, taxes, and expenses) over your compounding period. Investors, imbued with overconfidence, almost always make assumptions that are far too bullish.

Since 1926, according to Ibbotson Associates, U.S. stocks have earned an annual average of 9.8%. Their long-term, net-net-net return is under 4%.

All other major assets earned even less. If, like most people, you mix in some bonds and cash, your net-net-net is likely to be more like 2%.

Mr. Zweig points out that many investors, even some institutional investors, are assuming net-net-net returns of 7% or more. When he asked truly sophisticated investors what return they thought was reasonable, he got very different answers.

I asked several investing experts what guaranteed net-net-net return they would accept to swap out their own assets. William Bernstein of Efficient Frontier Advisors would take 4%. Laurence Siegel, a consultant and former head of investment research at the Ford Foundation: 3%. John C. Bogle, founder of the Vanguard Group of mutual funds: 2.5%. Elroy Dimson of London Business School, an expert on the history of market returns: 0.5%.

The reality is pretty shocking, isn’t it? This is why the investor has an uphill battle. And the consequences of messing any of the four steps up along the way can be pretty steep. In Mr. Zweig’s eloquent words,

The faith in fancifully high returns isn’t just a harmless fairy tale. It leads many people to save too little, in hopes that the markets will bail them out. It leaves others to chase hot performance that cannot last. The end result of fairy-tale expectations, whether you invest for yourself or with the help of a financial adviser, will be a huge shortfall in wealth late in life, and more years working rather than putting your feet up in retirement.

Saving too little can become a big problem. I would add that ruining your returns by thrashing about impulsively will only add to the amount you will need to save. Almost everyone has a number in mind for the amount of assets they will need in retirement. Try redoing the math with realistic numbers and see if you are really saving enough.

—-this article originally appeared 1/19/2010. Americans are still under-saving to an alarming extent. Given that we are currently in a very low yield environment, a high savings level is more important than ever.